Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS

Wang, Mengxia; Fu, Boya; Fan, Jianbo; Wang, Yi; Zhang, Liankuan; Xia, Chunlei

doi:10.1016/j.ecoinf.2022.101931

Cited by 37 publications

(11 citation statements)

References 25 publications

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“…As discussed by Li et al [29], the inconsistent sizes of rectangular labeling boxes and labeling di culties could also decrease model performance. The presence of weeds, young leaves, and complex leaf scenes resulting in appearance distortion could lead to detection failures and increased FP ratings [30]. However, under the eld conditions examined in this study, our model was not affected by plants other than beans, as the elds exhibited good agronomic practices to control weeds (See Fig.…”

Section: Plant Detection Accuracymentioning

confidence: 94%

“…Deep convolutional neural network models also have been widely applied as a reliable analytical method to detection and counting of target structure in several crops [28][29][30][31]. In particular, Faster R-CNN has shown promise as an effective method for detecting plant structures and/or counting individual plants using drone images [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

Volpato

Wright

Gomez

2023

Preprint

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Background Significant effort has been made in manually tracking plant maturity and to measure early-stage plant density, and crop height in experimental breeding plots. Agronomic traits such as relative maturity (RM), stand count (SC) and plant height (PH) are essential to cultivar development, production recommendations and management practices. The use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost. Recent advances in deep learning (DL) approaches have enabled the development of automated high-throughput phenotyping (HTP) systems that can quickly and accurately measure target traits using low-cost RGB drones. In this study, a time series of drone images was employed to estimate dry bean relative maturity (RM) using a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for features extraction and capturing the sequential behavior of time series data. The performance of the Faster-RCNN object detection algorithm was also examined for stand count (SC) assessment during the early growth stages of dry beans. Various factors, such as flight frequencies, image resolution, and data augmentation, along with pseudo-labeling techniques, were investigated to enhance the performance and accuracy of DL models. Traditional methods involving pre-processing of images were also compared to the DL models employed in this study. Moreover, plant architecture was analyzed to extract plant height (PH) using digital surface model (DSM) and point cloud (PC) data sources. Results The CNN-LSTM model demonstrated high performance in predicting the RM of plots across diverse environments and flight datasets, regardless of image size or flight frequency. The DL model consistently outperformed the pre-processing images approach using traditional analysis (LOESS and SEG models), particularly when comparing errors using mean absolute error (MAE), providing less than two days of error in prediction across all environments. When growing degree days (GDD) data was incorporated into the CNN-LSTM model, the performance improved in certain environments, especially under unfavorable environmental conditions or weather stress. However, in other environments, the CNN-LSTM model performed similarly to or slightly better than the CNN-LSTM + GDD model. Consequently, incorporating GDD may not be necessary unless weather conditions are extreme. The Faster R-CNN model employed in this study was successful in accurately identifying bean plants at early growth stages, with correlations between the predicted SC and ground truth (GT) measurements of 0.8. The model performed consistently across various flight altitudes, and its accuracy was better compared to traditional segmentation methods using pre-processing images in OpenCV and the watershed algorithm. An appropriate growth stage should be carefully targeted for optimal results, as well as precise boundary box annotations. On average, the PC data source marginally outperformed the CSM/DSM data to estimating PH, with average correlation results of 0.55 for PC and 0.52 for CSM/DSM. The choice between them may depend on the specific environment and flight conditions, as the PH performance estimation is similar in the analyzed scenarios. However, the ground and vegetation elevation estimates can be optimized by deploying different thresholds and metrics to classify the data and perform the height extraction, respectively. Conclusions The results demonstrate that the CNN-LSTM and Faster R-CNN deep learning models outperforms other state-of-the-art techniques to quantify, respectively, RM and SC. The subtraction method proposed for estimating PH in the absence of accurate ground elevation data yielded results comparable to the difference-based method. In addition, open-source software developed to conduct the PH and RM analyses can contribute greatly to the phenotyping community.

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Section: Plant Detection Accuracymentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

Volpato

Wright

Gomez

2023

Preprint

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“…Object detection techniques can be broadly categorized into traditional algorithms and deep learning-based approaches [4]. Traditional object detection methods [5] [6] were conventionally utilized for equipment identification and anomaly detection in substations prior to the advent of deep learning-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Anomaly detection method of substation equipment based on CenterNet

Zhang,

Sun

2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

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Deep learning has significantly advanced object detection algorithms in the area of abnormal detection for substation equipment. However, there are still several challenges and problems that hinder its effective application in this domain. Firstly, the scale of data acquisition is limited, which may affect the performance of deep learning models. Secondly, there are various types and shapes of substation equipment with complex backgrounds, which will also affect the detection performance of deep learning models. This paper proposes an improved CenterNet-based method for target detection of substation equipment. Firstly, we embedded a Non-Local attention mechanism in the backbone network ResNet50. The integration of the attention mechanism can effectively improve the model's ability to prioritize significant targets and minimize the negative impact of intricate backgrounds on its overall performance. Then, the DIOU loss is used to replace the center point offset loss and width-height loss to accelerate network convergence and improve model performance. The proposed method exhibits superior performance in substation equipment anomaly detection, as evidenced by experimental results, with a detection accuracy increase of 10.8% over the original method and outperforming existing techniques like Faster R-CNN.

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“…This area has attracted extensive research from scholars who have contributed many innovative and impactful methods. As described in relevant literature, these modeling approaches accurately identify crops and weeds, thereby enhancing crop yields and reducing the use of chemical herbicides, consequently mitigating adverse ecological impacts [3][4][5] . However, despite the wealth of research, significant challenges in actual agricultural environments remain inadequately addressed: these models perform suboptimally when processing real-world data containing noise.…”

Section: Introductionmentioning

confidence: 99%

Improved DINO Model-Based Approach for ObjectDetection of Sesame Seedlings and Weeds

Wang,

2024

Preprint

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In the application scenario of precise pesticide spraying on sesame seedlings, existing object detection models exhibit lowrecognition accuracy and detection efficiency due to the complex imaging environment and the limited computational powerof agricultural intelligent equipment. Addressing this issue, this study proposes a denoising and lightweight object detectionmethod for sesame seedling and weed detection based on the DINO model. By introducing MobileNetV3 to replace theoriginal ResNet-50 backbone network in DINO, the neural network layers of the model are reduced by 70%, and the number ofparameters is decreased from 47M to 39M. Furthermore, the study employs multi-scale feature extraction from the 6th, 9th,and 13th layers of MobileNetV3. On the COCO dataset, the model achieves a recognition accuracy of 34.3% for targets smallerthan 32x32, which is an improvement of 2.3% over the original DINO model. The overall recognition accuracy is increasedby 2.1%, with a 17% reduction in parameters. Additionally, the model incorporates a three-layer lightweight image denoisingpreprocessing network based on the N2N strategy, achieving a peak signal-to-noise ratio of 34.52dB under level 10 Gaussiannoise. Furthermore, the model is trained and tested on a custom noise-added sesame seedling and weed dataset, achievinga recognition accuracy of 81.8% and a detection speed of 24 frames per second, which is a 5.6% improvement over thewell-established object detection model YOLOv7. The study demonstrates that the improved DINO object detection model hassignificant advantages in accuracy and lightweight design.

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Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS

Cited by 37 publications

References 25 publications

Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

Anomaly detection method of substation equipment based on CenterNet

Improved DINO Model-Based Approach for ObjectDetection of Sesame Seedlings and Weeds

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